Update app.py

app.py

@@ -1,21 +1,19 @@
-import numpy as np
 import torch
-from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
-from transformers import BertForSequenceClassification,BertTokenizer
-
+import numpy as np
 import gradio as gr
 from typing import Dict
-
+from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
+from transformers import BertForSequenceClassification,BertTokenizer
 
 num_labels=14
+#Loading Model and Tokenizer from Hugging Face Spaces
 model = BertForSequenceClassification.from_pretrained("owaiskha9654/Multi-Label-Classification-of-PubMed-Articles", num_labels=num_labels)
 tokenizer = BertTokenizer.from_pretrained('owaiskha9654/Multi-Label-Classification-of-PubMed-Articles', do_lower_case=True) 
 
 
-def Multi_Label_Classification_of_Pubmed_Articles(model_input: str) -> Dict[str, float]:
-    
+def Multi_Label_Classification_of_Pubmed_Articles(model_input: str) -> Dict[str, float]: #This wrapper function will pass the article into the model
     dict_custom={}
-    Preprocess_part1=model_input[:len(model_input)]
+    Preprocess_part1=model_input[:len(model_input)] #splitting inputext into 2 parts
     Preprocess_part2=model_input[len(model_input):]
     dict1=tokenizer.encode_plus(Preprocess_part1,max_length=1024,padding=True,truncation=True)
     dict2=tokenizer.encode_plus(Preprocess_part2,max_length=1024,padding=True,truncation=True)